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arXiv Machine Learning By Ayush Mohanty, Nazal Mohamed, Nagi Gebraeel

Uncertainty Quantification in Federated Granger Causality Learning

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The paper studies uncertainty quantification in federated Granger causality learning, where multiple parties cannot share data but need to infer predictive dependencies across clients. It addresses the challenge that clients observe different features and that cross-client edges must be estimated indirectly through iterative client‑server communication, which propagates both aleatoric and epistemic uncertainty. By deriving closed‑form variance recursions and steady‑state variances, the authors show that initial model‑parameter uncertainty diminishes asymptotically, enabling statistically principled selection of genuine cross‑client dependencies and improving edge recovery in synthetic and real industrial datasets.

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